1. Field of the Invention
The present invention relates to an image processing apparatus that performs image processing of a medical image so that the image can easily be used for, e.g., diagnosis.
2. Description of the Related Art
In recent years, in a medical field, endoscopes have come to be widely used for performing, e.g., examinations and/or diagnosis.
Also, methods for performing image processing of a medical image picked up via an endoscope by inserting the endoscope into a lumen inside a patient's body, using patterns of microstructures of submucosal vessels and/or mucous surfaces by means of a computer-used diagnosis support system so that surgeons can more easily and effectively use the image for, e.g., diagnosis.
In this case, it is necessary to quantify the patterns.
For example, Japanese Patent Application Laid-Open Publication No. 2005-34211, which is a first conventional example, discloses the following content: a partial image of interest is extracted from image data, and which density distribution pattern, from among density distribution patterns stored in density distribution pattern storing means, each pixel in a unit pixel group in the extracted partial image of interest is similar to is determined, and appearance frequency counts of the respective density distribution patterns appearing in the partial image of interest are done for the respective density distribution patterns to figure out a pattern histogram.
Then, the pattern histogram is repeatedly presented to learning means to make the learning means learn weights connecting an input node and an output node, and learned weight vectors connecting output nodes and respective input nodes, which have been learned, is obtained as a representing pattern histogram.
Japanese Patent Application Laid-Open Publication No. 2006-239270, which is a second conventional example, discloses the following content: a differential image between a first breast image, which is a diagnosis target, and a second breast image, which is a reference for determining a diseased region in the first breast image, is obtained, and a plurality of regions of interest ROI are set inside a lung-field region in the differential image.
Disease information indicating whether or not each of the regions of interest ROI has an interstitial lung disease is obtained to determine whether or not the lung-field region is a diseased region according to a frequency per unit area of the regions of interest ROI indicated as being an interstitial lung disease appearing in the lung-field region.